The Collective Good: Pooling Data to Boost Brain Imaging Research

The Collective Good: Pooling Data to Boost Brain Imaging Research

The world of chronic pain research now has its eyes on the brain. Some quality of the brain—whether a particular gray matter distribution or the idiosyncratic configuration of a network—might be the key to the perpetuation of pain perception long after an initial injury, according to a growing number of studies.

However, finding that cerebral essence is hindered by the fact that most brain imaging studies of chronic pain are limited to small numbers of patients due to cost and practicality. Several researchers are now aiming to get more from those studies by establishing new resources that allow the sharing of magnetic resonance imaging (MRI) data.

One such effort is the Pain and Interoception Imaging Network (PAIN), headed by Emeran Mayer, a gastroenterologist and neuroscientist at the University of California, Los Angeles, US. PAIN provides the infrastructure for researchers to share resting-state functional MRI data and structural MRI data from patients with different chronic pain conditions, whether it be irritable bowel syndrome, lower back pain, or migraine. PAIN is the first standardized brain imaging database dedicated to chronic pain.

PAIN “will function as the lead site for collecting brain imaging data,” said Mayer, where results from multiple groups will be pooled in one hub. The repository began on a smaller scale as part of the Multidisciplinary Approaches to Pelvic Pain (MAPP) Neuroimaging Network, part of a research consortium funded by the US National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to study pelvic pain. The new PAIN resource is funded by the US National Center for Complementary and Alternative Medicine (NCCAM) and the National Institute on Drug Abuse (NIDA). Other collaborators include the Laboratory of Neuro Imaging (LONI) of the University of Southern California, and the UCLA Center for Neurobiology of Stress.

A key feature of this database is the metadata, as Mayer calls it. In addition to brain images, information about experimental design and the patient's or participant’s clinical, behavioral, and genetic data is also uploaded. So far, imaging and metadata from several hundred patients representing typical phenotypes of different kinds of chronic pain have been added to the repository through the efforts of 15 partner institutions in the United States and Europe. PAIN’s goal is to collect scans and metadata from a total of 1,000 chronic pain patients after acquiring additional funding. The metadata will allow researchers to link disease states to brain biomarkers.

The PAIN network has two repositories, one for prospective studies that will adhere to standardized data acquisition parameters set by PAIN, called the PAIN Standardized Repository. Standardization of imaging will allow for sophisticated analyses combining data across sites. A second resource, the PAIN Archive Repository, has a more open policy where any researcher may upload already collected resting-state brain scans of chronic pain patients or healthy controls. So far, the network has focused on collecting three types of MRI scans, including high-quality structural images, diffusion tensor imaging data, and resting-state functional imaging data.

The standardized database, “is not a totally open repository,” said Mayer. Researchers apply to be members and then agree to comply with PAIN imaging standards, including the requirement to contribute 20 scans per year. Members then have access to the full combined data in the repository.

“Neuroimaging data are very specific to an investigator,” said Angela Laird, a cognitive neuroscientist and medical physicist at Florida International University. Laird is co-leader of the BrainMap project, a database of published functional and structural neuroimaging experiments. Neuroimaging results depend on the investigator’s experimental parameters, as Laird points out. This is a reason why the PAIN network uploads details about experimental design along with imaging data.

Mayer’s and other participating members’ hope is that the increased number of brain scans of different chronic pain patients will help researchers sort out the finer distinctions within chronic pain subtypes. Patients often present with multiple kinds of chronic pain as assessed by their symptomatology, some variations of which may be overlooked in smaller studies. With researchers pooling their participant data together, new subcategories of chronic pain might be discovered based on the underlying biology. Chronic pain conditions that are now classified by a patient’s body of symptoms may become recognized as the overlapping of several biologically unique disorders.

Additionally, PAIN provides tools and resources for researchers to interpret the collected data. The future vision for the network is to delineate brain signatures that characterize each unique chronic pain state. It is expected that with such precise information, researchers can eventually correlate individual brain signatures with genetics, epigenetics, behavioral data, and clinical parameters.

Another pain researcher is heading up a similar effort to create a neuroimaging database for pain research. Vania Apkarian, a neuroscientist at Northwestern University Feinberg School of Medicine, Chicago, US is introducing OpenPain to pain researchers.

OpenPain is a collaboration among Northwestern University, the US National Institute on Drug Abuse (NIDA), and the US National Institute of Neurological Disorders and Stroke (NINDS) to promote the sharing of brain imaging data for the purpose of pain research.

Even though Apkarian is a participating member of Mayer’s PAIN network, he has designed OpenPain to be a little different. As indicated by its title, OpenPain will be open to any researcher who has published brain imaging data related to pain to share. “To make serious progress in the field, we need to put resources together and create something that we can all use,” said Apkarian. He is leading by example by uploading brain imaging data he has collected in his own research. “It will be open access, no limits, and no requirements.”

Unlike PAIN, the OpenPain database does not require metadata of any specific type, but simply any data collected from patients and participants used for published research, such as demographics, scanning parameters, and experimental procedures. Apkarian’s hope is that open-access collaboration will break down barriers among academic circles and encourage increased cooperation among researchers.

Most pain research is publicly funded, so “the authors owe the public to make these data openly available,” Apkarian said. OpenPain will be a Web-based facilitator in that exchange. Currently, the database is designed to allow researchers to share resting-state and task-related functional MRI, and diffusion tensor imaging data, similar to the PAIN network.

OpenPain is in the process of launching its servers in the next few weeks, with contributions anticipated from many pain researchers, Apkarian said.

This migration from single studies to shared data is not without precedent in the greater scientific community. Pooling data affords researchers greater statistical power to draw stronger conclusions at no additional cost. “I like the idea of databasing because it promotes data discovery: new ways of analyzing the data we haven’t tried yet,” said Laird. With databases, the interdisciplinary community can see brain data in new ways.

Many fields of science are headed in this direction, including genomics, microbiomics, and most recently, brain science. Biomedical research as a whole is moving toward big data. “The era of ‘Big Data’ has arrived,” said Francis Collins during a recent announcement that welcomed the NIH’s first Associate Director for Data Science. The PAIN network and OpenPain are two strides in that same direction.